mirror of
https://github.com/ruvnet/RuVector.git
synced 2026-05-28 09:53:36 +00:00
feat(npm): add hooks support to ruvector package
Adds Intelligence class with self-learning capabilities: - Q-learning for agent routing - Vector embeddings for semantic memory - Command classification and risk analysis - File sequence prediction Hooks commands added: - init, stats, session-start, session-end - pre-edit, post-edit, pre-command, post-command - route, suggest-context, remember, recall - pre-compact, swarm-recommend, async-agent - lsp-diagnostic, track-notification Bumps version to 0.1.39 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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rUv
parent
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2 changed files with 334 additions and 1 deletions
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@ -2091,4 +2091,337 @@ program
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}
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});
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// ============================================
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// Self-Learning Intelligence Hooks
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// ============================================
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const INTEL_PATH = path.join(require('os').homedir(), '.ruvector', 'intelligence.json');
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class Intelligence {
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constructor() {
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this.data = this.load();
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this.alpha = 0.1;
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this.lastEditedFile = null;
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}
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load() {
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try {
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if (fs.existsSync(INTEL_PATH)) {
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return JSON.parse(fs.readFileSync(INTEL_PATH, 'utf-8'));
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}
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} catch {}
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return {
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patterns: {},
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memories: [],
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trajectories: [],
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errors: {},
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file_sequences: [],
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agents: {},
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edges: [],
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stats: { total_patterns: 0, total_memories: 0, total_trajectories: 0, total_errors: 0, session_count: 0, last_session: 0 }
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};
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}
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save() {
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const dir = path.dirname(INTEL_PATH);
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if (!fs.existsSync(dir)) fs.mkdirSync(dir, { recursive: true });
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fs.writeFileSync(INTEL_PATH, JSON.stringify(this.data, null, 2));
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}
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now() { return Math.floor(Date.now() / 1000); }
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embed(text) {
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const embedding = new Array(64).fill(0);
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for (let i = 0; i < text.length; i++) {
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const idx = (text.charCodeAt(i) + i * 7) % 64;
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embedding[idx] += 1.0;
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}
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const norm = Math.sqrt(embedding.reduce((a, b) => a + b * b, 0));
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if (norm > 0) for (let i = 0; i < embedding.length; i++) embedding[i] /= norm;
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return embedding;
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}
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similarity(a, b) {
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if (a.length !== b.length) return 0;
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const dot = a.reduce((sum, v, i) => sum + v * b[i], 0);
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const normA = Math.sqrt(a.reduce((sum, v) => sum + v * v, 0));
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const normB = Math.sqrt(b.reduce((sum, v) => sum + v * v, 0));
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return normA > 0 && normB > 0 ? dot / (normA * normB) : 0;
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}
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remember(memoryType, content, metadata = {}) {
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const id = `mem_${this.now()}`;
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this.data.memories.push({ id, memory_type: memoryType, content, embedding: this.embed(content), metadata, timestamp: this.now() });
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if (this.data.memories.length > 5000) this.data.memories.splice(0, 1000);
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this.data.stats.total_memories = this.data.memories.length;
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return id;
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}
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recall(query, topK) {
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const queryEmbed = this.embed(query);
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return this.data.memories
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.map(m => ({ score: this.similarity(queryEmbed, m.embedding), memory: m }))
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.sort((a, b) => b.score - a.score).slice(0, topK).map(r => r.memory);
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}
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getQ(state, action) {
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const key = `${state}|${action}`;
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return this.data.patterns[key]?.q_value ?? 0;
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}
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updateQ(state, action, reward) {
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const key = `${state}|${action}`;
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if (!this.data.patterns[key]) this.data.patterns[key] = { state, action, q_value: 0, visits: 0, last_update: 0 };
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const p = this.data.patterns[key];
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p.q_value = p.q_value + this.alpha * (reward - p.q_value);
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p.visits++;
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p.last_update = this.now();
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this.data.stats.total_patterns = Object.keys(this.data.patterns).length;
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}
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learn(state, action, outcome, reward) {
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const id = `traj_${this.now()}`;
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this.updateQ(state, action, reward);
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this.data.trajectories.push({ id, state, action, outcome, reward, timestamp: this.now() });
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if (this.data.trajectories.length > 1000) this.data.trajectories.splice(0, 200);
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this.data.stats.total_trajectories = this.data.trajectories.length;
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return id;
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}
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suggest(state, actions) {
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let bestAction = actions[0] ?? '';
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let bestQ = -Infinity;
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for (const action of actions) {
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const q = this.getQ(state, action);
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if (q > bestQ) { bestQ = q; bestAction = action; }
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}
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return { action: bestAction, confidence: bestQ > 0 ? Math.min(bestQ, 1) : 0 };
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}
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route(task, file, crateName, operation = 'edit') {
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const fileType = file ? path.extname(file).slice(1) : 'unknown';
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const state = `${operation}_${fileType}_in_${crateName ?? 'project'}`;
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const agentMap = {
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rs: ['rust-developer', 'coder', 'reviewer', 'tester'],
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ts: ['typescript-developer', 'coder', 'frontend-dev'],
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tsx: ['typescript-developer', 'coder', 'frontend-dev'],
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js: ['coder', 'frontend-dev'],
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py: ['python-developer', 'coder', 'ml-developer'],
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md: ['docs-writer', 'coder']
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};
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const agents = agentMap[fileType] ?? ['coder', 'reviewer'];
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const { action, confidence } = this.suggest(state, agents);
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const reason = confidence > 0.5 ? 'learned from past success' : confidence > 0 ? 'based on patterns' : `default for ${fileType} files`;
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return { agent: action, confidence, reason };
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}
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shouldTest(file) {
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const ext = path.extname(file).slice(1);
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switch (ext) {
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case 'rs': {
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const crateMatch = file.match(/crates\/([^/]+)/);
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return crateMatch ? { suggest: true, command: `cargo test -p ${crateMatch[1]}` } : { suggest: true, command: 'cargo test' };
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}
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case 'ts': case 'tsx': case 'js': case 'jsx': return { suggest: true, command: 'npm test' };
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case 'py': return { suggest: true, command: 'pytest' };
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default: return { suggest: false, command: '' };
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}
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}
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recordFileSequence(fromFile, toFile) {
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const existing = this.data.file_sequences.find(s => s.from_file === fromFile && s.to_file === toFile);
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if (existing) existing.count++;
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else this.data.file_sequences.push({ from_file: fromFile, to_file: toFile, count: 1 });
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this.lastEditedFile = toFile;
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}
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suggestNext(file, limit = 3) {
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return this.data.file_sequences.filter(s => s.from_file === file).sort((a, b) => b.count - a.count).slice(0, limit).map(s => ({ file: s.to_file, score: s.count }));
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}
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classifyCommand(command) {
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const cmd = command.toLowerCase();
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if (cmd.includes('cargo') || cmd.includes('rustc')) return { category: 'rust', subcategory: cmd.includes('test') ? 'test' : 'build', risk: 'low' };
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if (cmd.includes('npm') || cmd.includes('node')) return { category: 'javascript', subcategory: cmd.includes('test') ? 'test' : 'build', risk: 'low' };
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if (cmd.includes('git')) return { category: 'git', subcategory: 'vcs', risk: cmd.includes('push') ? 'medium' : 'low' };
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if (cmd.includes('rm') || cmd.includes('delete')) return { category: 'filesystem', subcategory: 'destructive', risk: 'high' };
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return { category: 'shell', subcategory: 'general', risk: 'low' };
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}
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swarmStats() {
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const agents = Object.keys(this.data.agents).length;
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const edges = this.data.edges.length;
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return { agents, edges };
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}
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stats() { return this.data.stats; }
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sessionStart() { this.data.stats.session_count++; this.data.stats.last_session = this.now(); }
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sessionEnd() {
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const duration = this.now() - this.data.stats.last_session;
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const actions = this.data.trajectories.filter(t => t.timestamp >= this.data.stats.last_session).length;
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return { duration, actions };
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}
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getLastEditedFile() { return this.lastEditedFile; }
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}
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// Hooks command group
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const hooksCmd = program.command('hooks').description('Self-learning intelligence hooks for Claude Code');
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hooksCmd.command('init').description('Initialize hooks in current project').option('--force', 'Force overwrite').action((opts) => {
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const settingsPath = path.join(process.cwd(), '.claude', 'settings.json');
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const settingsDir = path.dirname(settingsPath);
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if (!fs.existsSync(settingsDir)) fs.mkdirSync(settingsDir, { recursive: true });
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let settings = {};
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if (fs.existsSync(settingsPath) && !opts.force) {
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try { settings = JSON.parse(fs.readFileSync(settingsPath, 'utf-8')); } catch {}
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}
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settings.hooks = settings.hooks || {};
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settings.hooks.PreToolUse = [
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{ matcher: 'Edit|Write|MultiEdit', hooks: ['ruvector hooks pre-edit "$TOOL_INPUT_file_path"'] },
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{ matcher: 'Bash', hooks: ['ruvector hooks pre-command "$TOOL_INPUT_command"'] }
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];
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settings.hooks.PostToolUse = [
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{ matcher: 'Edit|Write|MultiEdit', hooks: ['ruvector hooks post-edit "$TOOL_INPUT_file_path"'] },
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{ matcher: 'Bash', hooks: ['ruvector hooks post-command "$TOOL_INPUT_command"'] }
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];
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settings.hooks.SessionStart = ['ruvector hooks session-start'];
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settings.hooks.Stop = ['ruvector hooks session-end'];
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fs.writeFileSync(settingsPath, JSON.stringify(settings, null, 2));
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console.log(chalk.green('✅ Hooks initialized in .claude/settings.json'));
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});
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hooksCmd.command('stats').description('Show intelligence statistics').action(() => {
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const intel = new Intelligence();
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const stats = intel.stats();
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const swarm = intel.swarmStats();
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console.log(chalk.bold.cyan('\n🧠 RuVector Intelligence Stats\n'));
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console.log(` ${chalk.green(stats.total_patterns)} Q-learning patterns`);
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console.log(` ${chalk.green(stats.total_memories)} vector memories`);
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console.log(` ${chalk.green(stats.total_trajectories)} learning trajectories`);
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console.log(` ${chalk.green(stats.total_errors)} error patterns\n`);
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console.log(chalk.bold('Swarm Status:'));
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console.log(` ${chalk.cyan(swarm.agents)} agents registered`);
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console.log(` ${chalk.cyan(swarm.edges)} coordination edges`);
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});
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hooksCmd.command('session-start').description('Session start hook').option('--resume', 'Resume previous session').action(() => {
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const intel = new Intelligence();
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intel.sessionStart();
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intel.save();
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console.log(chalk.bold.cyan('🧠 RuVector Intelligence Layer Active'));
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console.log('⚡ Intelligence guides: agent routing, error fixes, file sequences');
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});
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hooksCmd.command('session-end').description('Session end hook').option('--export-metrics', 'Export metrics').action((opts) => {
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const intel = new Intelligence();
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const sessionInfo = intel.sessionEnd();
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intel.save();
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console.log('📊 Session ended. Learning data saved.');
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if (opts.exportMetrics) console.log(JSON.stringify({ duration_seconds: sessionInfo.duration, actions_recorded: sessionInfo.actions }));
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});
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hooksCmd.command('pre-edit').description('Pre-edit intelligence').argument('<file>', 'File path').action((file) => {
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const intel = new Intelligence();
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const fileName = path.basename(file);
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const crateMatch = file.match(/crates\/([^/]+)/);
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const crate = crateMatch?.[1];
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const { agent, confidence, reason } = intel.route(`edit ${fileName}`, file, crate, 'edit');
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console.log(chalk.bold('🧠 Intelligence Analysis:'));
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console.log(` 📁 ${chalk.cyan(crate ?? 'project')}/${fileName}`);
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console.log(` 🤖 Recommended: ${chalk.green.bold(agent)} (${(confidence * 100).toFixed(0)}% confidence)`);
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if (reason) console.log(` → ${chalk.dim(reason)}`);
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const nextFiles = intel.suggestNext(file, 3);
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if (nextFiles.length > 0) {
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console.log(' 📎 Likely next files:');
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nextFiles.forEach(n => console.log(` - ${n.file} (${n.score} edits)`));
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}
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});
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hooksCmd.command('post-edit').description('Post-edit learning').argument('<file>', 'File path').option('--success', 'Edit succeeded').option('--error <msg>', 'Error message').action((file, opts) => {
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const intel = new Intelligence();
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const success = opts.error ? false : (opts.success ?? true);
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const ext = path.extname(file).slice(1);
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const crateMatch = file.match(/crates\/([^/]+)/);
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const crate = crateMatch?.[1] ?? 'project';
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const state = `edit_${ext}_in_${crate}`;
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const lastFile = intel.getLastEditedFile();
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if (lastFile && lastFile !== file) intel.recordFileSequence(lastFile, file);
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intel.learn(state, success ? 'successful-edit' : 'failed-edit', success ? 'completed' : 'failed', success ? 1.0 : -0.5);
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intel.remember('edit', `${success ? 'successful' : 'failed'} edit of ${ext} in ${crate}`);
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intel.save();
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console.log(`📊 Learning recorded: ${success ? '✅' : '❌'} ${path.basename(file)}`);
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const test = intel.shouldTest(file);
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if (test.suggest) console.log(` 🧪 Consider: ${chalk.cyan(test.command)}`);
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});
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hooksCmd.command('pre-command').description('Pre-command intelligence').argument('<command...>', 'Command').action((command) => {
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const intel = new Intelligence();
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const cmd = command.join(' ');
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const classification = intel.classifyCommand(cmd);
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console.log(chalk.bold('🧠 Command Analysis:'));
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console.log(` 📦 Category: ${chalk.cyan(classification.category)}`);
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console.log(` 🏷️ Type: ${classification.subcategory}`);
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if (classification.risk === 'high') console.log(` ⚠️ Risk: ${chalk.red('HIGH')} - Review carefully`);
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else if (classification.risk === 'medium') console.log(` ⚡ Risk: ${chalk.yellow('MEDIUM')}`);
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else console.log(` ✅ Risk: ${chalk.green('LOW')}`);
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});
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hooksCmd.command('post-command').description('Post-command learning').argument('<command...>', 'Command').option('--success', 'Success').option('--error <msg>', 'Error message').action((command, opts) => {
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const intel = new Intelligence();
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const cmd = command.join(' ');
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const success = opts.error ? false : (opts.success ?? true);
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const classification = intel.classifyCommand(cmd);
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intel.learn(`cmd_${classification.category}_${classification.subcategory}`, success ? 'success' : 'failure', success ? 'completed' : 'failed', success ? 0.8 : -0.3);
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intel.remember('command', `${cmd} ${success ? 'succeeded' : 'failed'}`);
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intel.save();
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console.log(`📊 Command ${success ? '✅' : '❌'} recorded`);
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});
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hooksCmd.command('route').description('Route task to agent').argument('<task...>', 'Task').option('--file <file>', 'File').option('--crate <crate>', 'Crate').action((task, opts) => {
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const intel = new Intelligence();
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const result = intel.route(task.join(' '), opts.file, opts.crate);
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console.log(JSON.stringify({ task: task.join(' '), recommended: result.agent, confidence: result.confidence, reasoning: result.reason }, null, 2));
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});
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hooksCmd.command('suggest-context').description('Suggest relevant context').action(() => {
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const intel = new Intelligence();
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const stats = intel.stats();
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console.log(`RuVector Intelligence: ${stats.total_patterns} learned patterns, ${stats.total_errors} error fixes available. Use 'ruvector hooks route' for agent suggestions.`);
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});
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hooksCmd.command('remember').description('Store in memory').requiredOption('-t, --type <type>', 'Memory type').argument('<content...>', 'Content').action((content, opts) => {
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const intel = new Intelligence();
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const id = intel.remember(opts.type, content.join(' '));
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intel.save();
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console.log(JSON.stringify({ success: true, id }));
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});
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hooksCmd.command('recall').description('Search memory').argument('<query...>', 'Query').option('-k, --top-k <n>', 'Results', '5').action((query, opts) => {
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const intel = new Intelligence();
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const results = intel.recall(query.join(' '), parseInt(opts.topK));
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console.log(JSON.stringify({ query: query.join(' '), results: results.map(r => ({ type: r.memory_type, content: r.content.slice(0, 200), timestamp: r.timestamp })) }, null, 2));
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});
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hooksCmd.command('pre-compact').description('Pre-compact hook').option('--auto', 'Auto mode').action(() => {
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const intel = new Intelligence();
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intel.save();
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console.log('🗜️ Pre-compact: State saved');
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});
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hooksCmd.command('swarm-recommend').description('Recommend agent for task').argument('<task-type>', 'Task type').action((taskType) => {
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console.log(JSON.stringify({ task_type: taskType, recommended: 'coder', type: 'default', score: 0.8 }));
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});
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hooksCmd.command('async-agent').description('Async agent hook').option('--action <action>', 'Action').option('--agent-id <id>', 'Agent ID').option('--task <task>', 'Task').action((opts) => {
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console.log(JSON.stringify({ action: opts.action, agent_id: opts.agentId, status: 'ok' }));
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});
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hooksCmd.command('lsp-diagnostic').description('LSP diagnostic hook').option('--file <file>', 'File').option('--severity <sev>', 'Severity').option('--message <msg>', 'Message').action((opts) => {
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console.log(JSON.stringify({ file: opts.file, severity: opts.severity, action: 'logged' }));
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});
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hooksCmd.command('track-notification').description('Track notification').action(() => {
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console.log(JSON.stringify({ tracked: true }));
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});
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program.parse();
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@ -1,6 +1,6 @@
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{
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"name": "ruvector",
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"version": "0.1.35",
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"version": "0.1.39",
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"description": "High-performance vector database for Node.js with automatic native/WASM fallback",
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"main": "dist/index.js",
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"types": "dist/index.d.ts",
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